{"id":"W2087867734","doi":"10.1109/allerton.2012.6483366","title":"Least-squares based adaptive source localization by mobile agents","year":2012,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Forgetting; Recursive least squares filter; Convergence (economics); Least-squares function approximation; Algorithm; Computer science; Stability (learning theory); Noise (video); Mathematical optimization; Least mean squares filter; Adaptive filter; Mathematics; Artificial intelligence; Statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002881783,0.0001720691,0.0001652363,0.00006538573,0.0001157247,0.0001330787,0.0006063531,0.00007623636,0.0001436668],"category_scores_gemma":[0.00003102535,0.0001536827,0.00006927975,0.0003091089,0.00002875145,0.000750357,0.0001041616,0.00006710737,0.0003377295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009291928,"about_ca_system_score_gemma":0.00003397967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001698086,"about_ca_topic_score_gemma":0.000005386486,"domain_scores_codex":[0.9984111,0.0001559955,0.000246447,0.0003048324,0.0004219882,0.0004597018],"domain_scores_gemma":[0.9989921,0.00007709883,0.0001122376,0.0005015782,0.00009732897,0.0002196856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008678751,0.002645073,0.03621937,0.0001002557,0.0002673616,0.00001447701,0.004977268,0.08280535,0.004944721,0.03381414,0.6967436,0.1373816],"study_design_scores_gemma":[0.0006507419,0.00006938748,0.0003861767,0.00001209344,0.000008056569,0.000002168416,0.0001891992,0.8688915,0.002995637,0.00001027795,0.1265586,0.0002260817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001562979,0.0001995807,0.9944303,0.0001714622,0.0003924019,0.0004082783,0.00002131019,0.0003599024,0.002453767],"genre_scores_gemma":[0.9948843,8.828312e-7,0.002136216,0.001003919,0.00007097601,0.0001029191,0.00004599893,0.00001381073,0.001741034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9933212,"threshold_uncertainty_score":0.6266997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01706487656473811,"score_gpt":0.2389257880554191,"score_spread":0.221860911490681,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}